Human driver beats robot car . . . barely

In a car race between man and machine, man won — but only by a few seconds. The contest was part of a project at Stanford University’s Center for Automotive Research (CAR) to develop control systems that will make autonomous cars more intuitive.

The autonomous car, affectionately referred to as “Shelley” after French rally driver Michèle Mouton, features a bevy of sensors capable of working out the vehicle’s position on the road, the grip of its tires, and more. All of this information is then used to plot the best route around the circuit.

Professor Chris Gerdes, head of CAR explained that the Thunderhill track was chosen because it features 15 turns which, in turn, presents the car’s control systems with a variety of challenges. For instance, some corners can be taken at high speed while others are a bit sharp and require more caution.

"What human drivers do consistently well is feel out the limits of the car and push it just a little bit further and that is where they have an advantage," said Professor Gerdes.

The math behind being able to make a car steer safely at high speed around a tight bend is pretty similar to that which is needed when trying to keep a vehicle on the road after it hits a patch of ice. Both instances, Gerdes explains, involve a calculation based on how much friction there is between the tires and road.

"As we set up these systems in the future, it's important not to build autonomous vehicles that are merely a collection of systems designed for human support but to think a little bit more holistically about making them as good as the very best human drivers," said Professor Gerdes. "It's not so much the technology as the capability of the human that is our inspiration now."